Using probabilistic relational learning to support bronchial carcinoma diagnosis based on ion mobility spectrometry

International Journal for Ion Mobility Spectrometry

Volumn 13, Issue 2, 2010, Pages 83-93

  Finthammer, Marc ^a   Beierle, Christoph ^a   Fisseler, Jens ^a   Kern Isberner, Gabriele ^b   Baumbach, Jörg Ingo ^c  

^a UNIVERSITY OF HAGEN   (Germany)

^b TU DORTMUND UNIVERSITY   (Germany)

^c KIST Europe ^*  (Germany)

Author keywords

Biomedical diagnosis; Ion mobility spectrometry; Knowledge representation; Markov logic networks; Probabilistic relational learning

Indexed keywords

BIOMEDICAL ENGINEERING; COMPUTER CIRCUITS; DIAGNOSIS; IONS; KNOWLEDGE REPRESENTATION; MARKOV PROCESSES; PROBABILISTIC LOGICS; SPECTROMETRY;

BIOMEDICAL DIAGNOSIS; BRONCHIAL CARCINOMA; CLASSIFICATION ACCURACY; ION MOBILITY SPECTRA; ION MOBILITY SPECTROMETRY; LOGICAL REPRESENTATIONS; MARKOV LOGIC NETWORKS; RELATIONAL LEARNING;

ION MOBILITY SPECTROMETERS;

EID: 77955227398     PISSN: 14356163     EISSN: 18654584     Source Type: Journal    
DOI: 10.1007/s12127-010-0042-9     Document Type: Article

References (15)

1. 2-an integrated medical software system for early lung cancer detection using ion mobility spectometry data of human breath. J Integr Bioinform 4(3): 75.
2. Baumbach JI, Westhoff M (2006) Ion mobility spectometry to detect lung cancer and airway infections. Spectrosc Eur 18(6): 22-27.
3. Bödeker B, Vautz W, Baumbach JI (2008) Peak finding and referencing in MCC/IMS-data. Int J Ion Mobil Spectrom 11(1-4): 83-87.
4. Bödeker B, Vautz W, Baumbach JI (2008) Visualization of MCC/IMS-data. Int J Ion Mobil Spectrom 11(1-4): 77-81.
5. Dehaspe L, Toironen H (2000) Discovery of relational association rules. In: Relational data mining. Springer, pp 189-208.
6. Finthammer M, Beierle C, Fisseler J, Kern-Isberner G, Möller B, Baumbach JI (2010) Probabilistic relational learning for medical diagnosis based on ion mobility spectrometry. In: Hüllermeier E, Kruse R, Hoffmann F (eds) Information processing and management of uncertainty in knowledge-based systems. Communications in computer and information science, vol 80. Springer, pp 365-375.
7. Getoor L, Taskar B (eds) (2007) Introduction to statistical relational learning. MIT.
8. Kersting K, De Raedt L (2007) Bayesian logic programming: theory and tool. In: An introduction to statistical relational learning. MIT.
9. Kok S, Singla P, Richardson M, Domingos P, Sumner M, Poon H, Lowd D, Wang J (2008) The Alchemy system for statistical relational AI: user manual. Department of Computer Science and Engineering, University of Washington.
10. MacQueen JB (1967) Some methods of classification and analysis of multivariate observations. In: Proceedings of the fifth berkeley symposium on mathematical statistics and probability, pp 281-297.
11. Möller B (2009) Probabilistisches logikbasiertes Lernen und Modellieren für die Atemgasanalyse. Diploma thesis, Technische Universität Dortmund.
12. Muggleton S, De Raedt L (1994) Inductive logic programming: theory and methods. J Log Program 19/20: 629-679.
13. Pearl J (1988) Probabilistic reasoning in intelligent systems. Morgan Kaufmann, San Mateo, Ca.
14. Richardson M, Domingos P (2006) Markov logic networks. Mach Learn 62(1): 107-136.
15. Srinivasan A (2007) The Aleph manual. www. comlab. ox. ac. uk/activities/machinelearning/Aleph/.